We analyse the evolution of the second wave of the COVID-19 pandemic in several countries by using a logistic model. The model uses a regression analysis based on the least-squares fitting. In particular, the growth rate of the infection has been fitted as an exponential increase, as compared to a power law increase, reported previously in logistic models. The data shows that the increase in the exponent of the exponential increase is around 0.03 day
 $$^{-1}$$
 , with a standard deviation of 0.01  day
 $$^{-1}$$
 . The present results suggest that duration of the peaking of the second wave is almost same for several countries considered. The growth rate is also on the same order of several countries regardless of the total number of infections in a particular country. Since the decay of the growth rate is self-similar to that during the increase in the second wave of several countries, we can predict the end of the second wave in India. The model suggests that the second wave will end in the first week of August 2021, with a growth rate of 0.1% day
 $$^{-1}$$
 at that time.

/pdf/analysis-of-second-wave-of-covid-19-in-different-countries-2qgw58u7aq.pdf

Analysis of Second Wave of COVID-19 in Different Countries

In this study, we consider a robust optimal parameter estimation method for the Susceptible-Unidentified infected-Confirmed (SUC) epidemic dynamics model. One of the problems in determining parameter values associated with epidemic mathematical models is that the optimal parameter values are very sensitive to the initial guess of parameter values. To resolve this problem, we fix the value of one parameter and solve an optimization problem of finding the other parameter values which best fit the confirmed population. The fixed parameter value can be obtained using data from epidemiological surveillance systems. To demonstrate the robustness and accuracy of the proposed method, we perform various numerical experiments with synthetic and real-world data from South Korea, the United States of America, India, and Brazil. The computational results confirm the potential practical application of the proposed method.

Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model

In site seismic characterization, peak ground acceleration (PGA) is an important parameter, along with response spectrum/time history, etc. Expanding strong motion network has been complemented by data sharing through satellite links. PGA is generally estimated using empirical expressions developed from this data (earthquake magnitude, distance from the site, etc.) and these have been updated periodically with fresh records. Certain studies added new factor/s specific to site, soil conditions, etc. for getting improved estimates of PGA. However, as estimation of PGA involves multiple uncertainties, the accuracy achieved by these equations leaves ample scope of improvement. In this article, development of data-driven model/s for estimation of PGA is presented using artificial neural network, using readily available recorded data from the literature. The best ANN models had a correlation between 0.86 and 0.89, mean absolute error between 18 and 25 gals, and root mean square error between 36 and 44 gals, indicating good performance. Out of 32 test data, only 2 (6%) of the estimates fell outside the one standard deviation limits on either side of the perfect fit for Set 1, one (3%) each in case of Set 2 and Set 3, and this can be considered to be a pretty good fit. The case study for Himalayan region in India would demonstrate comprehensive model evaluation followed by an intuitive ranking system to select the best model from a few candidate models.

PGA Estimation for Indian Himalayan Region Using ANN: Ranking Approach for Selection of Best Model

Chloride ion is an important indicator of water quality. Field measurement of chloride is difficult whereas laboratory measurement is both time-consuming and chemical intensive. The conservative nature of chloride and good correlation with electrical conductivity (EC) justifies its use as proxy for chloride estimations. Comparison of the best regression models (RMs) and data-driven decision tree (DT) model enables appreciation of relative merits of the two approaches for this purpose. Quantitative improvements over the models from literature are, increase in correlation (RM: 0.70 to 0.77; DT: 0.70 to 0.78) and decrease in relative errors (RM: MARE: 0.88 to 0.65 and RMSRE: 1.91 to 0.92; DT: MARE: 0.88 to 0.40; RMSRE: 1.91 to 0.54); thereby, DT has emerged as the better modeling approach for this case. Considering the influence of seasonality (pre-or post-monsoon) and degree of saturation of soil (water logged or water depleted) enabled the reduction of the correlation range (0.24-0.87) of the basic variables to a smaller range (0.44-0.89) for estimates of Cl-, along with relative error ranging from 0.35 to 0.57, the improvement being more pronounced for lower value of variable correlations. The overall comparison using the evaluation datasets between RM from literature and RM/DT models from this study exemplified that for the study area, the case-specific models developed using the data-driven tool: DT resulted in the most accurate estimation of chloride in groundwater from the chosen proxy: EC.

https://link.springer.com/content/pdf/10.1007/s10661-021-09577-y.pdf

Decision tree for estimating groundwater contaminant through proxies considering seasonality and soil saturation.

Development of site-specific time distribution pattern of rainfall for Tarapur, India

Analysis of trend of epidemiological data helps to appreciate the progression of an epidemic and to develop monitoring and control strategies by the government agencies. Sen's Innovative Method suggests a graphical analysis, which can overcome many limitations of data such as short length, non-Gaussian nature, skewness or serial correlation. In this article, this method is applied for the first time on epidemiological data. For the case study, Covid-19 or SARS-CoV-2 data from India were employed. The results show that Sen's Innovative Method is capable of indicating the shift in epidemiological trend quite efficiently, before it is reflected in the time series or moving average plots. The graphical analysis worked particularly well in comparing the trends of monthly data. It is concluded that this method would be especially suitable for monitoring the epidemiological trend by breaking up the data into smaller segments, as was illustrated in the study.

/pdf/sen-s-innovative-method-for-trend-analysis-of-epidemic-a-2x2tuh9g9k.pdf

Sen's Innovative Method for Trend Analysis of Epidemic: A Case Study of Covid-19 Pandemic in India

Underground blasts are conducted for deep excavations, tunneling, or mining activities. Scaled distance regression analysis is performed in industry to estimate peak particle velocity from charge weight and distance. For addressing the uncertainties in estimating safe charge weight for controlled blasting, 95% confidence expression is generally used. For addressing inaccuracies arising from superimposition of blast waves in multi-hole blasting when using attenuation equation developed from single-hole blast data, a modified approach was proposed in literature. This article presents comparisons to establish that industrial practice of scaled distance regression would be as satisfactory as the proposed modified approach, when various performance measures (including parsimony) are considered together. Furthermore, industrial practice of using 95% confidence expression generated from sufficient data (say, 40 numbers) would result in safe charge weight estimation, whereas modified scaled distance approach (mean expression) could still result in few non-conservative values.

A Re-Look Into Modified Scaled Distance Regression for Prediction of Blast-Induced Ground Vibration

Continuous daily rainfall records are generally available for most locations in India. In the absence of generic temporal distribution of rainfall for any site, hydraulic designers resort to factors obtained from temporal distribution curves in the literature to arrive at design n-hour rainfall value (n < 24) from the design daily extreme rainfall. In literature, adoption of temporal distribution from site data has been advocated for important structures. In this article, rainfall records from a western coastal site in India are first used to establish that the 1-h factors would be comparable when obtained from 53-year (1961–2013) annual extreme rainfall data and 17-year (1997–2013) monthly monsoon extreme rainfall data. It was shown that the 1-h factor estimated from short period monthly records could yield better estimates when applied on longer period annual maxima to derive 1-h maximum rainfall, when compared to the factors from generic temporal distribution curves in literature. Using this philosophy, the n-hour factors up to 6-h maximum continuous rainfall are estimated from the continuous hourly rainfall records available for the site (1997–2013). The n-hour factor was higher for lower daily rainfall value, up to 6 h. Temporal distribution factors for n-hour (n ≤ 6) continuous rainfall have been recommended for various maximum daily rainfall ranges, which, when compared to those from literature, would be better suited for this site, and possibly for the surrounding region as well.

Saha Dauji

Papers

Sen's Innovative Method for Trend Analysis of Epidemic: A Case Study of Covid-19 Pandemic in India

A Re-Look Into Modified Scaled Distance Regression for Prediction of Blast-Induced Ground Vibration

Site-Specific Factors to Derive Maximum 1-hourly to 6-hourly Continuous Rainfall from Daily Extreme Rainfall on West Coast of India